• Journal of Gifted/Talented Education
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• v.25 no.6
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• pp.857-879
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• 2015

This study investigated the relationships among middle school students' perceptions on the roles of online tutor, their deep learning, achievement, and overall evaluation of learning experiences in the context of inquiry based online gifted mathematics and science learning. For this purpose, 249 middle school students who took online course were surveyed about their perceptions on the degree to which their tutor performed the roles as an online tutor. The students were also asked about the activities which indicate deep learning approaches and overall course experiences such as the level of satisfaction, understanding and engagement in the course. The regression analyses were conducted to examine the relationships of students' perceptions on the roles of online tutor, deep learning, achievement, and overall course experiences. The results first showed that the roles of online tutor which affects students' deep learning approach such as high-order learning, integrative learning, reflective learning were the role as a subject matter and evaluation expert. Among the sub variables of deep learning approach the variable that was related to students' overall achievement was the use of high-order learning strategy. Second, the achievement in inquiry task was related to the role of tutor as a guide of learning process and method. Third, students' overall course evaluations such as the level of satisfaction, understanding and engagement were not related to any role of tutor.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.11a
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• pp.805-807
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• 2021

In-hospital cardiac arrest is a significant problem for medical systems. Although the traditional early warning systems have been widely applied, they still contain many drawbacks, such as the high false warning rate and low sensitivity. This paper proposed a strategy that involves a deep learning approach based on a novel interpretable deep tabular data learning architecture, named TabNet, for the Rapid Response System. This study has been processed and validated on a dataset collected from two hospitals of Chonnam National University, Korea, in over 10 years. The learning metrics used for the experiment are the area under the receiver operating characteristic curve score (AUROC) and the area under the precision-recall curve score (AUPRC). The experiment on a large real-time dataset shows that our method improves compared to other machine learning-based approaches.

• KIPS Transactions on Computer and Communication Systems
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• v.10 no.7
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• pp.191-198
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• 2021

Since the size of training dataset become large and the model is getting deeper to achieve high accuracy in deep learning, the deep neural network training requires a lot of computation and it takes too much time with a single node. Therefore, distributed deep learning is proposed to reduce the training time by distributing computation across multiple nodes. In this study, we propose hybrid allreduce strategy that considers the characteristics of each layer and communication and computational overlapping technique for synchronization of distributed deep learning. Since the convolution layer has fewer parameters than the fully-connected layer as well as it is located at the upper, only short overlapping time is allowed. Thus, butterfly allreduce is used to synchronize the convolution layer. On the other hand, fully-connecter layer is synchronized using ring all-reduce. The empirical experiment results on PyTorch with our proposed scheme shows that the proposed method reduced the training time by up to 33% compared to the baseline PyTorch.

• Journal of Korean Elementary Science Education
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• v.26 no.3
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• pp.286-294
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• 2007

This study investigated the relationship between elementary school students' creative problem solving skills in terms of science and cognitive strategies. Creative problem solving in science was measured by 4 variables; appropriateness, scientific ability, concreteness, and originality. Cognitive strategies were measured by 6 variables; surface(rehearsal), deep(elaboration and organization), and metacognitive strategies(planning, monitoring, and regulating). The KEDI Creative Problems Solving Test in Science(Cho et al., 1997) and the Motivated Strategies for Learning Questionnaire(Pintrich & DeGroot, 1990) were administered to 72 subjects. Data were analyzed by means of Pearson's correlation and multiple regression analysis. Our findings indicated a positive correlation between creative problem solving in science and cognitive strategies. The surface cognitive strategy (rehearsal) positively predicted the total score, the scientific ability's score, the concrete score, and the original score of creative problem solving in science. The deep cognitive strategy(organization) positively predicted the appropriate score and the metacognitive strategy(planning) positively predicted the original score of scientific creative problem solving skills.

• Nuclear Engineering and Technology
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• v.56 no.4
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• pp.1472-1479
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• 2024

Cyber-Physical Energy Systems (CPESs) integrate cyber and hardware components to ensure a reliable and safe physical power production and supply. Renewable Energy Sources (RESs) add uncertainty to energy demand that can be dealt with flexible operation (e.g., load-following) of CPES; at the same time, scenarios that could result in severe consequences due to both component stochastic failures and aging of the cyber system of CPES (commonly overlooked) must be accounted for Operation & Maintenance (O&M) planning. In this paper, we make use of Deep Reinforcement Learning (DRL) to search for the optimal O&M strategy that, not only considers the actual system hardware components health conditions and their Remaining Useful Life (RUL), but also the possible accident scenarios caused by the failures and the aging of the hardware and the cyber components, respectively. The novelty of the work lies in embedding the cyber aging model into the CPES model of production planning and failure process; this model is used to help the RL agent, trained with Proximal Policy Optimization (PPO) and Imitation Learning (IL), finding the proper rejuvenation timing for the cyber system accounting for the uncertainty of the cyber system aging process. An application is provided, with regards to the Advanced Lead-cooled Fast Reactor European Demonstrator (ALFRED).

• Journal of Information Processing Systems
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• v.20 no.3
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• pp.360-374
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• 2024

A personalized course recommendation algorithm based on deep learning in an online education cloud platform is proposed to address the challenges associated with effective information extraction and insufficient feature extraction. First, the user potential preferences are obtained through the course summary, course review information, user course history, and other data. Second, by embedding, the word vector is turned into a low-dimensional and dense real-valued vector, which is then fed into the compressed interaction network-deep neural network model. Finally, considering that learners and different interactive courses play different roles in the final recommendation and prediction results, an attention mechanism is introduced. The accuracy, recall rate, and F1 value of the proposed method are 0.851, 0.856, and 0.853, respectively, when the length of the recommendation list K is 35. Consequently, the proposed strategy outperforms the comparison model in terms of recommending customized course resources.

• Korean Journal of Remote Sensing
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• v.38 no.6_2
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• pp.1581-1587
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• 2022

Recently, deep learning has become more important in remote sensing data processing. Huge amounts of data for artificial intelligence (AI) has been designed and built to develop new technologies for remote sensing, and AI models have been learned by the AI training dataset. Artificial intelligence models have developed rapidly, and model accuracy is increasing accordingly. However, there are variations in the model accuracy depending on the person who trains the AI model. Eventually, experts who can train AI models well are required more and more. Moreover, the deep learning technique enables us to automate methods for remote sensing applications. Methods having the performance of less than about 60% in the past are now over 90% and entering about 100%. In this special issue, thirteen papers on how deep learning techniques are used for remote sensing applications will be introduced.

• Journal of The Korean Association For Science Education
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• v.25 no.3
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• pp.321-326
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• 2005

The purpose of this study was to identify distinctive achievement goal patterns of students and examine their influence on learning strategies (deep/surface) and science achievement. Cluster analysis procedure was performed to classify students on the basis of task, performance, and performance-avoidance goal scores. The results produced 3 clusters of students with different achievement goal patterns: high task goal (cluster 1), high task-high performance goal (cluster 2), and low task-low performance goal (cluster 3). One-way ANOVA results revealed that the scores of cluster 2 were significantly higher than those of clusters 1 and 3 in deep learning strategy. The science achievement test scores of clusters 1 and 2 were higher than those of cluster 3. Looking at surface learning strategy, however, the test scores of cluster 3 were significantly higher than those of clusters 1 and 2. The educational implications of these findings are discussed.

• Proceedings of the Korea Information Processing Society Conference
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• 2020.05a
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• pp.455-457
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• 2020

Recent studies demonstrate that deep learning model is easily biased by trained with unbalanced datasets. For example, the deep network can be trained to make a prediction by background feature instead the real target's feature. For those problem, a measurement called leakage was introduced to digitize this tendency. In this paper, we propose augmentation strategy which are used generally in computer vision problem to remedy this bias problem and we showed a simple augmentation methods have a effect to this task with experiments.

• The Journal of Korean Academy of Sensory Integration
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• v.16 no.3
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• pp.11-21
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• 2018

Objective : The purpose of this study is to investigate correlation of sensory processing patterns, learning styles and learning strategies for university students. Methods : Participants of this study are 115 students from K university in Busan, South Korea. Measurements are Adolescent/Adult Sensory Profile (AASP) for sensory processing patterns, the Study Process Questionnaire (SPQ) for learning styles, and the Motivated Strategies for Learning Questionnaire (MSLQ) for learning strategies. The data collected was analyzed by SPSS/WIN 20.0 for chisuare test and Pearson corelation coefficient. Results : For sensory processing patterns and learning styles, there were correlation between low registration type and surface type of learning (p=0.03), and between sensory seeking type and deep type of learning (p=0.02). For sensory processing patterns and learning strategies, sensory seeking type was correlated with organized learning strategy (p=0.00), and sensory sensitivity type was correlated with organizational learning strategy (p=0.03) and meta-cognitive learning strategy (p=0.00). Conclusion : This study found that there is correlation between sensory processing patterns, learning styles and learning strategies with implying learning styles and learning strategies can be different depends on sensory procession pattern. The results of this study can be used as a basic data to select learning type and learning strategy appropriate for an individual based on his or her sensory processing patterns.